This Graph here shows off the total rounds played of each player
ggplot(pstats, aes(y=TotalRoundsPlayed,x=Player_Name, fill=Player_Name)) + geom_bar(stat = "Identity") + geom_text(aes(label=TotalRoundsPlayed), vjust=1.6, color="white", size=3.5) + ggtitle("Total Rounds Played") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
overallPStats %>% filter(stat=="Rounds") %>% ggplot(aes(x=role,y=overall,fill=role))+geom_bar(stat = "Identity") + geom_text(aes(label=overall), vjust=1.6, color="black", size=3.5) + ggtitle("Rounds played as each role")
overallPStats %>% filter(stat=="Rounds" & role!="Innocent") %>% ggplot(aes(x="",y=overall/4715,fill=role)) +
geom_bar(stat="identity", width=1,color="white") +
coord_polar("y", start=0) +
theme_void()
avgWin %>% ggplot(aes(x=role,y=winPercent,fill=role))+geom_bar(stat = "Identity")+geom_text(aes(label=sprintf("%0.2f", round(winPercent, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=TraitorWins, y=TraitorRounds, fill=Player_Name)) + geom_point() + ggtitle("Traitor Rounds vs. Traitor wins") +
geom_label_repel(aes(label = Player_Name),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50') +
theme_classic()
pstats %>% group_by(Player_Name,TraitorRounds,TraitorWins) %>%
summarise(winPercent=TraitorWins/TraitorRounds) %>%
arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'TraitorRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, TraitorRounds [10]
## Player_Name TraitorRounds TraitorWins winPercent
## <fct> <dbl> <dbl> <dbl>
## 1 nuzzels 145 89 0.614
## 2 silentscout9909 128 58 0.453
## 3 Amazingbro17 94 37 0.394
## 4 King Nuggets 92 32 0.348
## 5 FunLiberal 91 31 0.341
## 6 ghostNINJA-72 83 24 0.289
## 7 MrBoy 87 24 0.276
## 8 Jeff_the_Shark 89 24 0.270
## 9 TARTARI 24 6 0.25
## 10 TheMathGeek_314 62 13 0.210
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=TraitorRounds/TotalRoundsPlayed) %>%
ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as traitor") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
ggplot(pstats,aes(x=InnocentWins, y=InnocentRounds, fill=Player_Name)) + geom_point() + ggtitle("Innocent Rounds vs. Innocent wins") +
geom_label_repel(aes(label = Player_Name),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50') +
theme_classic()
pstats %>% group_by(Player_Name,InnocentRounds,InnocentWins) %>%
summarise(winPercent=InnocentWins/InnocentRounds) %>%
arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'InnocentRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, InnocentRounds [10]
## Player_Name InnocentRounds InnocentWins winPercent
## <fct> <dbl> <dbl> <dbl>
## 1 silentscout9909 286 187 0.654
## 2 Amazingbro17 258 166 0.643
## 3 nuzzels 316 201 0.636
## 4 Jeff_the_Shark 265 168 0.634
## 5 FunLiberal 265 165 0.623
## 6 TARTARI 67 40 0.597
## 7 MrBoy 254 151 0.594
## 8 King Nuggets 259 153 0.591
## 9 ghostNINJA-72 213 123 0.577
## 10 TheMathGeek_314 191 108 0.565
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=InnocentRounds/TotalRoundsPlayed) %>%
ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as innocent") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=DetectiveWins, y=DetectiveRounds, fill=Player_Name)) + geom_point() + ggtitle("Detective Rounds vs. Detective wins") +
geom_label_repel(aes(label = Player_Name),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50') +
theme_classic()
pstats %>% group_by(Player_Name,DetectiveRounds,DetectiveWins) %>%
summarise(winPercent=DetectiveWins/DetectiveRounds) %>%
arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'DetectiveRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, DetectiveRounds [10]
## Player_Name DetectiveRounds DetectiveWins winPercent
## <fct> <dbl> <dbl> <dbl>
## 1 nuzzels 88 62 0.705
## 2 King Nuggets 59 38 0.644
## 3 ghostNINJA-72 49 30 0.612
## 4 Amazingbro17 74 45 0.608
## 5 TARTARI 12 7 0.583
## 6 MrBoy 69 40 0.580
## 7 TheMathGeek_314 42 24 0.571
## 8 silentscout9909 73 41 0.562
## 9 Jeff_the_Shark 66 36 0.545
## 10 FunLiberal 61 29 0.475
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=DetectiveRounds/TotalRoundsPlayed) %>%
ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as detective") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=Zombiewins, y=ZombieRounds, fill=Player_Name)) + geom_point() + ggtitle("Zombie Rounds vs. Zombie wins") +
geom_label_repel(aes(label = Player_Name),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50') +
theme_classic()
pstats %>% group_by(Player_Name,Zombiewins,ZombieRounds) %>%
summarise(winPercent=Zombiewins/ZombieRounds) %>%
arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'Zombiewins'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, Zombiewins [10]
## Player_Name Zombiewins ZombieRounds winPercent
## <fct> <dbl> <dbl> <dbl>
## 1 nuzzels 50 64 0.781
## 2 MrBoy 18 29 0.621
## 3 TheMathGeek_314 14 23 0.609
## 4 Jeff_the_Shark 22 37 0.595
## 5 Amazingbro17 16 29 0.552
## 6 ghostNINJA-72 11 20 0.55
## 7 FunLiberal 17 31 0.548
## 8 silentscout9909 25 46 0.543
## 9 King Nuggets 14 29 0.483
## 10 TARTARI 2 6 0.333
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=ZombieRounds/TotalRoundsPlayed) %>%
ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as zombie") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=HypnotistWins, y=HypnotistRounds, fill=Player_Name)) + geom_point() + ggtitle("Hypnotist Rounds vs. Hypnotist wins") +
geom_label_repel(aes(label = Player_Name),
box.padding = 0.35,
point.padding = 0.5,
segment.color = 'grey50') +
theme_classic()
pstats %>% group_by(Player_Name,HypnotistWins,HypnotistRounds) %>%
summarise(winPercent=HypnotistWins/HypnotistRounds) %>%
arrange(desc(winPercent))
## `summarise()` has grouped output by 'Player_Name', 'HypnotistWins'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, HypnotistWins [10]
## Player_Name HypnotistWins HypnotistRounds winPercent
## <fct> <dbl> <dbl> <dbl>
## 1 Amazingbro17 9 11 0.818
## 2 nuzzels 16 21 0.762
## 3 ghostNINJA-72 5 10 0.5
## 4 silentscout9909 4 9 0.444
## 5 MrBoy 6 14 0.429
## 6 Jeff_the_Shark 6 20 0.3
## 7 TheMathGeek_314 2 7 0.286
## 8 FunLiberal 3 12 0.25
## 9 King Nuggets 3 13 0.231
## 10 TARTARI 1 9 0.111
pstats %>% group_by(Player_Name) %>% summarise(percentPlayed=HypnotistRounds/TotalRoundsPlayed) %>%
ggplot(aes(y=percentPlayed,x=Player_Name,fill=Player_Name)) + geom_bar(stat = "Identity") + ggtitle("Percent of rounds played as hypnotist") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(percentPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=Player_Name, y=CrookedCop, fill=Player_Name)) + geom_bar(stat="identity") + geom_text(aes(label=CrookedCop), vjust=1.6, color="white", size=3.5) + ggtitle("Amount of Innocents killed as detective") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
pstats %>% group_by(Player_Name, DetectiveRounds, CrookedCop) %>%
summarise(Crookedeness=CrookedCop/DetectiveRounds) %>%
arrange(desc(Crookedeness))
## `summarise()` has grouped output by 'Player_Name', 'DetectiveRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, DetectiveRounds [10]
## Player_Name DetectiveRounds CrookedCop Crookedeness
## <fct> <dbl> <dbl> <dbl>
## 1 ghostNINJA-72 49 31 0.633
## 2 TARTARI 12 6 0.5
## 3 King Nuggets 59 29 0.492
## 4 FunLiberal 61 23 0.377
## 5 TheMathGeek_314 42 11 0.262
## 6 Amazingbro17 74 18 0.243
## 7 Jeff_the_Shark 66 13 0.197
## 8 MrBoy 69 13 0.188
## 9 silentscout9909 73 9 0.123
## 10 nuzzels 88 10 0.114
ggplot(pstats,aes(x=Player_Name, y=TriggerHappyInnocent, fill=Player_Name)) + geom_bar(stat="identity") + geom_text(aes(label=TriggerHappyInnocent), vjust=1.6, color="white", size=3.5) + ggtitle("Amount of Innocents killed as an Innocent") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
pstats %>% group_by(Player_Name, InnocentRounds, TriggerHappyInnocent) %>%
summarise(Shootyness=TriggerHappyInnocent/InnocentRounds) %>%
arrange(desc(Shootyness))
## `summarise()` has grouped output by 'Player_Name', 'InnocentRounds'. You can override using the `.groups` argument.
## # A tibble: 10 × 4
## # Groups: Player_Name, InnocentRounds [10]
## Player_Name InnocentRounds TriggerHappyInnocent Shootyness
## <fct> <dbl> <dbl> <dbl>
## 1 ghostNINJA-72 213 83 0.390
## 2 King Nuggets 259 71 0.274
## 3 MrBoy 254 62 0.244
## 4 FunLiberal 265 60 0.226
## 5 nuzzels 316 56 0.177
## 6 silentscout9909 286 50 0.175
## 7 TARTARI 67 11 0.164
## 8 Jeff_the_Shark 265 34 0.128
## 9 Amazingbro17 258 33 0.128
## 10 TheMathGeek_314 191 19 0.0995
ggplot(pstats,aes(x=Player_Name,y=KilledFirst,fill=Player_Name)) + geom_bar(stat = "Identity") + geom_text(aes(label=KilledFirst), vjust=1.6, color="Blue", size=3.5) + ggtitle("Amount of times killed first") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
ggplot(pstats, aes(x=Player_Name, y=KilledFirst/TotalRoundsPlayed, fill=Player_Name)) +
geom_bar(stat="identity") + ggtitle("Percentage of times killed first") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))+geom_text(aes(label=sprintf("%0.2f", round(KilledFirst/TotalRoundsPlayed, digits = 2))), vjust=1.6, color="black", size=3.5)
ggplot(pstats,aes(x=Player_Name,y=TotalFallDamage, fill=Player_Name)) + geom_bar(stat = "Identity") + geom_text(aes(label=TotalFallDamage), vjust=1.6, color="Blue", size=3.5) + ggtitle("People least safe near ledges") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
dequipClean %>% group_by(stat) %>% summarise(overallTimesBought = sum(count)) %>%
ggplot(aes(x=stat,y=overallTimesBought)) + geom_bar(stat="Identity") + geom_text(aes(label=overallTimesBought), vjust=1.6, color="Red", size=2.5) + ggtitle("Times each detective equipment was bought") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
dequipClean %>% group_by(stat) %>% summarise(percentBought=sum(count)/1172) %>% arrange(desc(percentBought))
## # A tibble: 35 × 2
## stat percentBought
## <chr> <dbl>
## 1 Gold_Deagle 0.153
## 2 Body_Armor 0.119
## 3 T-Suitcase 0.0811
## 4 PHD 0.0708
## 5 Radar 0.0597
## 6 Command_Prompt 0.0546
## 7 Speed_Cola 0.0546
## 8 DoubleTap 0.0495
## 9 Mystery_Box 0.0418
## 10 Stamin-up 0.0341
## # … with 25 more rows
dequipClean %>% filter(count>0) %>% group_by(Player_Name, stat) %>% summarise(equipBought=n()) %>%
ggplot(aes(x=Player_Name,y=equipBought, fill=Player_Name)) + geom_bar(stat = "Identity") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
ggtitle("Number of different detective equipment bought")
## `summarise()` has grouped output by 'Player_Name'. You can override using the `.groups` argument.
dequipClean %>% ggplot(aes(x=Player_Name,y=count, fill=Player_Name)) + geom_bar(stat = "Identity") + facet_wrap(~stat, ncol=5, scales = "free_y") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
tequipClean %>% group_by(stat) %>% summarise(overallTimesBought = sum(count)) %>%
ggplot(aes(x=stat,y=overallTimesBought)) + geom_bar(stat="Identity") + geom_text(aes(label=overallTimesBought), vjust=1.6, color="Red", size=2.5) + ggtitle("Times each traitor equipment was bought") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))
tequipClean %>% group_by(stat) %>% summarise(percentBought=sum(count)/1172) %>% arrange(desc(percentBought))
## # A tibble: 72 × 2
## stat percentBought
## <chr> <dbl>
## 1 Body_Armor 0.374
## 2 Radar 0.192
## 3 PHD 0.116
## 4 Eagleflight 0.110
## 5 wallhack 0.104
## 6 Speed_Cola 0.0990
## 7 DoubleTap 0.0973
## 8 Antlion 0.0887
## 9 Katana 0.0785
## 10 Silenced_AWP 0.0776
## # … with 62 more rows
tequipClean %>% filter(count>0) %>% group_by(Player_Name, stat) %>% summarise(equipBought=n()) %>%
ggplot(aes(x=Player_Name,y=equipBought, fill=Player_Name)) + geom_bar(stat = "Identity") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
ggtitle("Number of different traitor equipment bought")
## `summarise()` has grouped output by 'Player_Name'. You can override using the `.groups` argument.
tequipClean %>% ggplot(aes(x=Player_Name,y=count, fill=Player_Name)) + geom_bar(stat = "Identity") + facet_wrap(~stat, ncol=6, scales = "free_y") +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5))